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How to put human intelligence back into artificial intelligence

The vision of Sidewalk Labs for Toronto’s waterfront endures. Just not as first envisioned.

The business model of this Google sister company is based on monitoring people’s movements and connecting the dots. But this week, before signing on the dotted line, Toronto tried to turn the tables on Sidewalk Labs.

Now, the watcher is being watched and its surveillance is under scrutiny.

That is as it should be. No entity should amass information without transparency and accountability.

Many of us were lulled into surrendering our private data and public metadata to private companies unquestioningly. Yet the more people learn about so-called surveillance capitalism — and its presumed Big Brother, artificial intelligence — the less they like about lacking control.

Against that backdrop of fear and loathing — without knowing — it’s worth re-examining some of our assumptions and reservations, apprehensions and misapprehensions about AI versus surveillance. Tempting as it is to conflate the two, they are distinctly different.

But the two are symbiotic, for there can be no AI without HI. That fundamental reality emerged in a forum last month sponsored by the LEAP-Pecaut Centre for Social Impact that looked at the potential for AI to power the non-profit sector.

Full disclosure: I sit on LEAP’s volunteer advisory board, and I moderated the AI panel, which allowed me to ask the questions I’ve been grappling with while Toronto has been engaged in a sidebar with Sidewalk Labs.

To be clear, our panel wasn’t revisiting the fraught relationship between Waterfront Toronto and Google’s sister company, but it was very much the elephant in the room as we considered the potential for AI to deliver the good, the bad, and the unseemly.

Social entrepreneurs in the room were looking for the good while keeping an eye on the bad. Part of LEAP’s mandate is to scale up non-profits using the latest business techniques, replicating and applying good ideas and successful models to a broader social market that helps the most people.

The audience heard Alisa Simon describe her work at Kids Help Phone, which uses data and neural networks (computerized systems that recognize patterns) to figure out which potentially suicidal kids should get help fastest, rather than languishing on hold.

And they listened to Nicolas Frosst, a research engineer working at Google Brain, talk about his role at the internet giant’s AI research team. He argued persuasively that public apprehensions are often a product of public perceptions that link artificial intelligence to surveillance capitalism and authoritarian governments.

“Their fear is that this tool will allow those people to do the same (bad) thing more effectively, and that’s a totally valid concern,” Frosst noted. “I am as afraid of a gun as I am of somebody holding it, but it’s still important to tease those two things apart and realize that the fear of the use of the tool is very different from the fear of the tool itself.

“It’s interesting that we often project onto this thing simply because it seems complicated and outside of our reach, so it seems powerful — and I don’t really understand it — and therefore someone will use it for bad.”

Fair point, albeit a doubled-edged analogy. When the National Rifle Association tells Americans that “guns don’t kill people, people kill people,” most of us are understandably skeptical.

Kathryn Hume, who heads business development for Borealis AI, argued that artificial intelligence boils down to rapid-fire math and computing power. The tools are neutral, and it’s up to humans to use them properly.

Too often, human intelligence lets us down on AI matters. Facebook continues to generate criticism for turning a blind eye — and turning a profit — when people and politicians are manipulated online with AI tools that are used and abused.

Google’s search engine also aspired to be neutral once upon a time — hence its early corporate credo, “Don’t be evil.” Decades later, the newspaper industry was disemboweled by Google when it repurposed (and re-platformed) journalistic content at no cost while siphoning off lucrative advertising dollars.

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AI has its own internal logic, even if it leaves us at the mercy of the law of unintended consequences. There is no stopping the march of AI, and surely no point squandering Canada’s head start, given that it is home to the third-largest concentration of AI experts in the world, with Toronto boasting the highest concentration of AI startups in the world.

That hasn’t stopped the all-too-human Luddites in Ontario’s Progressive Conservative government from slashing $20 million in funding for the Vector Institute’s research on artificial intelligence, and a further $4 million for CIFAR’s programs supporting advanced AI research. All the more reason to be vigilant about AI — not just the risks of getting ahead of ourselves, but also the perils of falling behind the rest of the pack.

The benefits and risks of Toronto’s new Quayside project with Sidewalk Labs remind us that there will always be reason to watch the watchers and mind the minders. But we must also be mindful of the potential for AI to do good — not just with commercial gain but social benefits — lest we all lose out.

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